Week 9 7/16/14
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Transcript of Week 9 7/16/14
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Week 97/16/14
Amari LewisAidean Sharghi
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Light field dataset
• Using the depth estimate provided by the Lytro compatible viewer software.
• See if we can use this information to increase object recognition.
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• The white pixels do not have depth value- due to occlusion, distance, surface angle or material.
• The darker the color (black or grey), the more accurate depth perception)
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bike
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vehicle
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building
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RGB-D cameras
• Sensing systems that capture RGB images along with per-pixel depth information.
• The white pixels do not have depth value- due to occlusion, distance, surface angle or material.
• The darker the color (black), the more accurate depth perception)
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Studying the depth information…
• RGBD – OBJ CALSSIFIATION• The process(RGB-D object recognition and detection): utilize sliding window detectors trained from object views
to assign class probabilities to pixels in every RGB-D frame. • Our ultimate goal is to find out a way that we can
incorporate the use of the depth estimation from light field images for object recognition.
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References• Holistic Scene Understanding for 3D Object Detection with RGBD
camera authors: Dahua Lin Sanja Fidler Raquel Urtasun• RGB-D Object Recognition and Detection – Artificial intelligence
University of Washington• Depth from Combining Defocus and Correspondence Using Light-
Field Cameras-University of California, Berkeley Authors: Michael Tao1, Sunil Hadap2, Jitendra Malik1, and Ravi Ramamoorthi• RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of
Indoor Environments authors: Peter Henry, Michael Krainin, Evan Herbst, Xiaofeng Ren, Dieter Fox